Evaluation of shunt physical examination by artificial intelligence

Evaluation of Physical Examination in the Detection of Arteriovenous Fistula Stenosis by deep learning - Evaluation of Physical Examination in the Detection of Arteriovenous Fistula Stenosis by deep learning

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000041587

Enrollment

220

Registered

2020-10-01

Start date

2020-10-01

Completion date

Unknown

Last updated

2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

chronic kidney disease

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Inpatient dialysis patients from a dialysis center (Gamagori Municipal Hospital, Gamagori, Japan) during 2020-2022.

Exclusion criteria

Exclusion criteria: 1) Patients on catheter dialysis 2) Patients with unstable circulatory dynamics 3) Patients deemed by the attending physician to be inappropriate for study participation on medical grounds

Design outcomes

Primary

Measure	Time frame
Correlation with ultrasound evaluation items and shunt contrast results	—

Secondary

Measure	Time frame
Presence of intervention event	—

Countries

Japan

Contacts

Public ContactKeisuke Ota

Gamagori City Hospital Department of Nephrology

keke20keke20@yahoo.co.jp81-533-66-2200

Outcome results